• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.88-95
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• 1998

Vibration analysis of rotating machinery can give an indication of possible faults thus allowing maintenance before further damage occurs. Current predictive maintenance system installed in Pyung-tak has the ability to diagnose the mechanical problems within the LNG Pump when the vibration exceeds preset overall alarm levels. In this study, LNG pump auto-diagnosis system based upon Windows NT and DSP Board is developed. This system analysis velocity signal acquired from dual accelerometer input monitor system to diagnose pump condition. Many plots which display machine condition are shown and features of vibration are stored in every time. If the fault is found, the system diagnoses automatically using expert system and trend monitoring. Operator checks pump condition intuitively using personal computer monitor.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.22 no.2
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• pp.142-151
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• 2019

Purpose: The aim of the study was to identify factors related to the recurrence of intussusception in pediatric patients. Methods: The medical charts of patients diagnosed with intussusception and treated at Dongsan Medical Center, between March 2015 to June 2017, were retrospectively reviewed. Univariate and multivariate analyses were performed. Results: Among 137 patients, 23 patients (16.8%) had a recurrent intussusception and 8 of these patients (6%) had more than 2 episodes of recurrence. The age at diagnosis was significantly different between the non-recurrence and recurrence group (p=0.026), with age >1 year at the time of diagnosis associated with a greater rate of recurrence (p=0.002). The time interval from symptom onset to the initial reduction (<48 vs. ${\geq}48$ hours) was significantly longer in the recurrence group (p=0.034) and patients in the recurrence group had higher levels of C-reactive protein (CRP) (p=0.024). Bloody stools and a history of infection were significantly more frequent in the non-recurrence group (p=0.001 and p<0.001, respectively). On stepwise regression analysis, age >1 year at the time of presentation (odds ratio [OR], 4.79; 95% confidence interval [CI], 1.56-14.06; p=0.016) and no history of infection (OR, 0.18; 95% CI, 0.06-0.58; p=0.004) were retained as predictors of recurrence. Conclusion: Patients with intussusception who are older than 1 year at diagnosis, have an elevated CRP level, a delay of ${\geq}48$ hours between symptom onset and the initial reduction, an absence of bloody stools, and no history of infection should be closely monitoring for symptoms and signs of a possible recurrence.

• Nutrition Research and Practice
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• v.9 no.3
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• pp.268-277
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• 2015

BACKGROUND/OBJECTIVES: Although it is crucial to identify those children likely to be treated in an appropriate nutrition rehabilitation programme and discharge them at the appropriate time, there is no golden standard for such identification. The current study examined the appropriateness of using Mid-Upper Arm Circumference for the identification, follow-up and discharge of malnourished children. We also assessed its discrepancy with the Weight-for-Height based diagnosis, the rate of recovery, and the discharge criteria of the children during nutrition rehabilitation. SUBJECTS/METHODS: The study present findings from 156 children (aged 6-59 months) attending a supplementary feeding programme at Makadara and Jericho Health Centres, Eastern District of Nairobi, Kenya. Records of age, weight, height and mid-upper arm circumference were selected at three stages of nutrition rehabilitation: admission, follow-up and discharge. The values obtained were then used to calculate z-scores as defined by WHO Anthro while estimating different diagnostic indices. RESULTS: Mid-upper arm circumference single cut-off (< 12.5 cm) was found to exhibit high values of sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio at both admission and discharge. Besides, children recorded higher rate of recovery at 86 days, an average increment of 0.98 cm at the rate of 0.14mm/day, and a weight gain of 13.49gm/day, albeit higher in female than their male counterparts. Nevertheless, children admitted on basis of low MUAC had a significantly higher MUAC gain than WH at 0.19mm/day and 0.13mm/day respectively. CONCLUSIONS: Mid-upper arm circumference can be an appropriate tool for identifying malnourished children for admission to nutrition rehabilitation programs. Our results confirm the appropriateness of this tool for monitoring recovery trends and discharging the children thereafter. In principle the tool has potential to minimize nutrition rehabilitation costs, particularly in community therapeutic centres in developing countries.

• Plant Journal
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• v.8 no.3
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• pp.49-54
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• 2012

In this study, the way how we can find the defects of motor windings in advance will be discussed. The magnetic flux spectrum in the high frequency region of the large motor was analyzed based on the actual fault practices related with motor windings. In case of defective motor relative amplitude ratio of the stator slot frequency to its sideband was very high compared to that of healthy motor. And the defective signal related with motor windings was indicated in advance in the magnetic flux spectrum prior to over 1 month before failure. Considering this aspect it can be estimated that magnetic flux spectrum in the high frequency region has the excellent predictive diagnostic capability.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.143-149
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• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.11-16
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• 2014

Accurate diagnosis and proper monitoring of cancer patients remain important obstacles for successful cancer treatment. The search for cancer biomarkers can aid in more accurate prediction of clinical outcome and may also reveal novel predictive factors and therapeutic targets. One such prognostic marker seems to be FOXA1. Many studies have shown that FOXA1 is strongly expressed in a vast majority of cancers, including breast cancer, in which high expression is associated with a good prognosis. In this review, we summarize the role of this transcription factor in the development and prognosis of breast cancer in the hope of providing insights into utility of FOXA1 as a novel biomarker.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.1-12
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• 2021

Reactor Coolant Pump (RCP) is core part of nuclear power plant to provide the forced circulation of reactor coolant for the removal of core heat. Properly monitoring vibration of RCP is a key activity of a successful predictive maintenance and can lead to a decrease in failure, optimization of machine performance, and a reduction of repair and maintenance costs. Here, we developed real-time RCP Vibration Analysis System (VAS) that web based platform using NoSQL DB (Mongo DB) to handle vibration data of RCP. In this paper, we explain how to implement digital signal process of vibration data from time domain to frequency domain using Fast Fourier transform and how to design NoSQL DB structure, how to implement web service using Java spring framework, JavaScript, High-Chart. We have implement various plot according to standard of the American Society of Mechanical Engineers (ASME) and it can show on web browser based on HTML 5. This data analysis platform shows a upgraded method to real-time analyze vibration data and easily uses without specialist. Furthermore to get better precision we have plan apply to additional machine learning technology.